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Databricks Certified Data Analyst Associate Exam Sample Questions (Q46-Q51):

NEW QUESTION # 46
A data analyst is processing a complex aggregation on a table with zero null values and their query returns the following result:

Which of the following queries did the analyst run to obtain the above result?

Answer: E

Explanation:
The result set provided shows a combination of grouping by two columns (group_1 and group_2) with subtotals for each level of grouping and a grand total. This pattern is typical of a GROUP BY ... WITH ROLLUP operation in SQL, which provides subtotal rows and a grand total row in the result set.
Considering the query options:
A) Option A: GROUP BY group_1, group_2 INCLUDING NULL - This is not a standard SQL clause and would not result in subtotals and a grand total.
B) Option B: GROUP BY group_1, group_2 WITH ROLLUP - This would create subtotals for each unique group_1, each combination of group_1 and group_2, and a grand total, which matches the result set provided.
C) Option C: GROUP BY group_1, group 2 - This is a simple GROUP BY and would not include subtotals or a grand total.
D) Option D: GROUP BY group_1, group_2, (group_1, group_2) - This syntax is not standard and would likely result in an error or be interpreted as a simple GROUP BY, not providing the subtotals and grand total.
E) Option E: GROUP BY group_1, group_2 WITH CUBE - The WITH CUBE operation produces subtotals for all combinations of the selected columns and a grand total, which is more than what is shown in the result set.
The correct answer is Option B, which uses WITH ROLLUP to generate the subtotals for each level of grouping as well as a grand total. This matches the result set where we have subtotals for each group_1, each combination of group_1 and group_2, and the grand total where both group_1 and group_2 are NULL.

NEW QUESTION # 47
Which of the following layers of the medallion architecture is most commonly used by data analysts?

A. Silver
B. Bronze
C. All of these layers are used equally by data analysts
D. Gold
E. None of these layers are used by data analysts

Answer: D

Explanation:
The gold layer of the medallion architecture contains data that is highly refined and aggregated, and powers analytics, machine learning, and production applications. Data analysts typically use the gold layer to access data that has been transformed into knowledge, rather than just information. The gold layer represents the final stage of data quality and optimization in the lakehouse. Reference: What is the medallion lakehouse architecture?

NEW QUESTION # 48
Which location can be used to determine the owner of a managed table?

A. Review the Owner field in the schema page using Data Explorer
B. Review the Owner field in the database page using Data Explorer
C. Review the Owner field in the table page using the SQL Editor
D. Review the Owner field in the table page using Catalog Explorer

Answer: D

Explanation:
In Databricks, to determine the owner of a managed table, you can utilize the Catalog Explorer feature. The steps are as follows:
Access Catalog Explorer:
In your Databricks workspace, click on the Catalog icon in the sidebar to open Catalog Explorer.
Navigate to the Table:
Within Catalog Explorer, browse through the catalog and schema to locate the specific managed table whose ownership you wish to verify.
View Table Details:
Click on the table name to open its details page.
Identify the Owner:
On the table's details page, review the Owner field, which displays the principal (user, service principal, or group) that owns the table.
This method provides a straightforward way to ascertain the ownership of managed tables within the Databricks environment. Understanding table ownership is essential for managing permissions and ensuring proper access control.

NEW QUESTION # 49
A data analyst has been asked to configure an alert for a query that returns the income in the accounts_receivable table for a date range. The date range is configurable using a Date query parameter.
The Alert does not work.
Which of the following describes why the Alert does not work?

A. The wrong query parameter is being used. Alerts only work with Date and Time query parameters.
B. Queries that use query parameters cannot be used with Alerts.
C. Queries that return results based on dates cannot be used with Alerts.
D. Alerts don't work with queries that access tables.
E. The wrong query parameter is being used. Alerts only work with drogdown list query parameters, not dates.

Answer: B

Explanation:
The reason the alert is not functioning as expected is because Databricks SQL Alerts do not support query parameters. This limitation applies to all types of parameters, including date parameters.
Here's why:
Alerts require static, deterministic query results so they can compare values consistently during scheduled executions.
When a query includes parameters (e.g., a date range parameter), its results may change based on user input or the default value set in the query editor.
However, Databricks SQL Alerts will always use the default value set for the parameter at the time the alert is created. This means the alert doesn't dynamically adapt to new date ranges and will not reflect changes unless the query is manually updated.
As a result, if the business logic behind the alert depends on changing date ranges or any user input, the alert will not trigger correctly, or may never trigger at all.
Therefore, the correct explanation contradicts Option B, which is incorrect in saying that alerts cannot work with date-based queries at all. In fact, they can-as long as the query is static (i.e., without parameters).
Reference:
Databricks SQL Alerts Documentation
Databricks Knowledge: "You cannot use alerts with queries that contain parameters."

NEW QUESTION # 50
A data engineering team has created a Structured Streaming pipeline that processes data in micro-batches and populates gold-level tables. The microbatches are triggered every 10 minutes.
A data analyst has created a dashboard based on this gold level dat
a. The project stakeholders want to see the results in the dashboard updated within 10 minutes or less of new data becoming available within the gold-level tables.
What is the ability to ensure the streamed data is included in the dashboard at the standard requested by the project stakeholders?

A. A refresh schedule with an always-on SQL Warehouse (formerly known as SQL Endpoint
B. A refresh schedule with stakeholders included as subscribers
C. A refresh schedule with an interval of 10 minutes or less
D. A refresh schedule with a Structured Streaming cluster

Answer: C

Explanation:
In this scenario, the data engineering team has configured a Structured Streaming pipeline that updates the gold-level tables every 10 minutes. To ensure that the dashboard reflects the most recent data, it is essential to set the dashboard's refresh schedule to an interval of 10 minutes or less. This synchronization ensures that stakeholders view the latest information shortly after it becomes available in the gold-level tables. Options B, C, and D do not directly address the requirement of aligning the dashboard refresh frequency with the data update interval.

NEW QUESTION # 51
......

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